AI Instructional Prescriptiveness and Adaptive Performance: Evaluating the Instructional Agency Paradox Through Cognitive Dependency and Perceived Autonomy
DOI:
https://doi.org/10.33152/jmphss-10.3.5Keywords:
Algorithmic Management, AI Literacy, Cognitive Dependency, Adaptive Performance, Instructional Agency Paradox, Human-AI Collaboration.Abstract
This article examines the extent to which, AI instructional prescriptiveness (AIIP) impacts knowledge workers' adaptive performance (ADP) through cognitive dependency (CD) and perceived autonomy (PA). Moderating role of AI literacy was also examined. The focus is on the instructional agency paradox where AI driven instruction is counter to the human intelligence that the system is intended to augment. This study contains data from 202 knowledge workers using AI prompted training tool(s) in their work processes. A purposive sample of workers in information technology firms of Pakistan were examined. Survey was conducted and quantitative analyses have done by using SPSS 29 and PLS-SEM was used through Smart PLS4. The study identified that AI instructional prescriptiveness negatively predicts the adaptive performance. The mediating factors perceived autonomy and cognitive dependency partially mediated the relationship between AIIP and ADP. In addition, contrary to the techno-optimists, AI Literacy did not moderate the impact implying that upskilling is insufficient to counter design constraining systems. As algorithmic management becomes a norm in people management, learning and development managers and systems architects need to think beyond prescriptive data and move to supportive frameworks. Organizations have to understand that high friction, prescriptive AI will likely create a digitally skilled but cognitively dependent employee. This research contributes to the expanding body of literature on artificial intelligence by addressing the. autonomy and cognitive dependency pathways that explain performance eroding processes.
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